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All Journal TEKNIK INFORMATIKA Sainteks Jurnal Pseudocode SMATIKA Jurnal Ilmiah KOMPUTASI Jurnal Informatika JURNAL MEDIA INFORMATIKA BUDIDARMA InComTech: Jurnal Telekomunikasi dan Komputer Jurnal SOLMA Prosiding Seminar Nasional Teknoka Dinamisia: Jurnal Pengabdian Kepada Masyarakat SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan Jurnal Sisfokom (Sistem Informasi dan Komputer) IJID (International Journal on Informatics for Development) JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal Linguistik Komputasional Jurnal ICT : Information Communication & Technology Building of Informatics, Technology and Science FIKROH: JURNAL PEMIKIRAN DAN PENDIDIKAN ISLAM Jurnal Teknologi Dan Sistem Informasi Bisnis Zonasi: Jurnal Sistem Informasi Jurnal Informatika dan Rekayasa Perangkat Lunak Jurnal Asiimetrik: Jurnal Ilmiah Rekayasa Dan Inovasi JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Jurnal Sistem Komputer dan Informatika (JSON) Teknosains : Jurnal Sains,Teknologi dan Informatika Infotech: Journal of Technology Information Jurnal Pengabdian kepada Masyarakat Nusantara Jurnal Teknik Informatika (JUTIF) KLIK: Kajian Ilmiah Informatika dan Komputer Sibatik Journal : Jurnal Ilmiah Bidang Sosial, Ekonomi, Budaya, Teknologi, Dan Pendidikan Jurnal Penyuuhan dan Pemberdayaan Masyarakat (JPPM) STORAGE: Jurnal Ilmiah Teknik dan Ilmu Komputer SmartComp Fikroh: Jurnal Pemikiran dan Pendidikan Islam JURNAL TEKNIK INFORMATIKA DAN KOMPUTER Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Cosmic Jurnal Teknik BHAKTI JIVANA Smatika Jurnal : STIKI Informatika Jurnal
Firman Noor Hasan
Universitas Muhammadiyah Prof. Dr. Hamka
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Sentiment Analysis of Society Towards the Child-free Phenomenon (Life Without Children) on Twitter Using Naïve Bayes Algorithm Nurhaliza, Siti; Febriawan, Dimas; Hasan, Firman Noor
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 13 No. 1 (2024): MARET
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v13i1.1944

Abstract

The difference in societal perspective regarding personal well-being and understanding life choices is genuinely diverse. Lately, there is a prevalent thought where individuals believe that personal well-being can be achieved by choosing to live without children. Most of them prefer to prioritize their careers, education, or other activities that they believe can bring greater happiness and well-being to their lives. This topic has become a frequently discussed subject in almost every region of Indonesia, especially in urban areas. Not only facing negative stigma, the choice to live a life without children in Indonesia also carries positive connotations. Views on child-free in Indonesia are highly diverse, considering the many differences in social environments and each individual’s personal experiences. In this research, the Naïve Bayes algorithm is used as a sentiment classifier in the form of textual data collected through Twitter using the Rapid Miner. The data collection period spanned from May 3rd to May 10th, 2023. The research aims to analyze and present data regarding public sentiment towards the child-free phenomenon in Indonesia. The results of this research reveal the presence of 320 positive sentiments and 180 negative sentiments, with the accuracy value of the Naïve Bayes algorithm in conducting sentiment analysis on the child-free phenomenon reached 95.00%.
Utilization of the FP-Growth Algorithm on MSME Transaction Data:Recommendations for Small Gifts from The Padang Region Hasan, Firman Noor; Ariyansah, Riyan
JURNAL TEKNIK INFORMATIKA Vol. 17 No. 1: JURNAL TEKNIK INFORMATIKA
Publisher : Department of Informatics, Universitas Islam Negeri Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/jti.v17i1.37966

Abstract

The existence of adequate transaction data turns out to have a similar sales transaction pattern for MSMEs, so it would be a shame if it were left like that. Moreover, this data can be used to increase efficiency in MSMEs in the culinary sector, one of which is as a recommendation for small gifts. The study uses the Association Rules technique, whereas fp-growth is used to obtain a combination of elements. The goal is to facilitate MSMEs' ability to suggest small gifts to clients. The fp-growth algorithm calculation was implemented to process 2043 data originating from transaction data in MSMEs, with the specified minimum support value being 15%, while the minimum confidence value determined was 55%. The results of the trial obtained the two best rules, namely, "If a customer buys a list of small gifts from Balado Sanjai Chips, then the customer will buy Jangek Crackers" and "If a customer buys Jangek Crackers, then the customer will buy Sanjai Balado Chips".
A PELATIHAN DASAR PENGGUNAAN AI GENERATIF UNTUK MEMBANTU PRODUKSI KONTEN UMKM Widyastuti Andriyani; Prisilia Talakua; Lisa Astria Milasari; Firman Noor Hasan
Komunikasi Vol 2 No 2 (2025): Volume 2 No 2 Agustus 2025
Publisher : Forum Komunikasi Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65055/bhaktijivana.v2i2.36

Abstract

The purpose of this community service research is to measure the effectiveness of basic training on the use of generative AI in assisting content production for MSMEs in Yogyakarta. This training was designed to address the main challenges faced by MSMEs, namely limited resources and expertise in creating consistent and high-quality digital marketing content. Through an interactive workshop method focused on hands-on practice, 25 MSME actors were educated on the concept of generative AI, prompt engineering techniques, and the utilization of AI tools. The evaluation results showed a significant increase in participant understanding, content quality, and time efficiency, where participant understanding jumped from 15% to 95%, content quality increased by 60%, and time efficiency improved by 40%. In conclusion, this training proves that the integration of generative AI is an effective strategic solution for empowering MSMEs, enhancing their competitiveness, and fostering sustainable business growth in the digital era.
ANALISIS KEPUASAN MASYARAKAT TERHADAP PERILAKU KORUPSI PEMERINTAH BERDASARKAN KOMENTAR PADA SOSIAL MEDIA MENGGUNAKAN NAIVE BAYES CLASSIFIER Irwiensyah, Faldy; Hasan, Firman Noor
Infotech: Journal of Technology Information Vol 11, No 2 (2025): NOVEMBER
Publisher : ISTEK WIDURI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37365/jti.v11i2.576

Abstract

Corrupt behaviour by government officials often occurs and becomes a problem that can disturb the public and threaten the integrity of the government system. Social media has become an important means for the public to voice their opinions and sentiments on social issues, including corrupt behaviour by government officials. This study aims to analyze the corrupt behaviour of government officials based on public sentiment on social media using the Naïve Bayes Classifier method. Data was obtained from Twitter with keywords closely related to corruption cases involving government officials, data obtained in a certain period. The Naïve Bayes Classifier method was applied to classify tweets related to corrupt behaviour by government officials to later be categorized into positive sentiment, and negative sentiment. The results of this study conclude that the TF-IDF Weighting Process, 3 words are very dominant and often appear in public sentiments, namely the words "Corruption", "Official" and the word "Tax". This shows that the public is very angry and disappointed, and this results in a very low level of trust in corrupt behaviour carried out by government officials. Especially those carried out by tax officials
Analisis Sentimen Tanggapan Pengguna Aplikasi Bale by BTN Menggunakan Metode Support Vector Machine (SVM) Setiawan, Ahmat; Hasan, Firman Noor
STORAGE: Jurnal Ilmiah Teknik dan Ilmu Komputer Vol. 4 No. 4 (2025): November
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/storage.v4i4.6469

Abstract

Dalam era digital yang kian berkembang, analisis sentimen pada komentar pengguna dijadikan alat penting untuk mengevaluasi kualitas aplikasi mobile banking. Penelitian ini bertujuan untuk mengidentifikasi sentimen pengguna pada aplikasi bale by BTN yang diluncurkan pada Februari 2025 sebagai penyempurna dari aplikasi BTN Mobile. Metode yang digunakan meliputi scraping data ulasan dari Google Play Store, preprocessing teks (case folding, normalisasi, tokenisasi, stopword removal, dan stemming), pelabelan berdasarkan kamus lexicon-based approach, serta pembangunan klasifikasi model dengan algoritma Support Vector Machine dengan TF-IDF vectorization. Dari 2.000 data awal, diperoleh 1.767 data valid yang dianalisis. Hasil menunjukkan bahwa model SVM mencapai akurasi sebesar 73,16%, dari 354 data testing dengan distribusi sentimen: positif (52,57%), dan negatif (47,43%). Model menunjukkan performa terbaik dalam mengklasifikasi sentimen Positif dengan precision 0.73, recall 0.80, dan F1-score 0,77 pada 194 data sedangkan pada sentimen negatif, model menunjukan hasil cukup baik dengan precision 0.73, recall 0.65, dan F1-score 0.69 pada 160 data.
Analisis Sentimen Terhadap Kandidat Calon Presiden Berdasarkan Tweets Di Sosial Media Menggunakan Naive Bayes Classifier Allif Rizki Abdillah; Firman Noor Hasan
SMATIKA JURNAL : STIKI Informatika Jurnal Vol 13 No 01 (2023): SMATIKA Jurnal : STIKI Informatika Jurnal
Publisher : LPPM UBHINUS MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/smatika.v13i01.750

Abstract

This research is to analyze the sentiments of the Indonesian people about the presidential candidates who are likely to advance in the 2024 presidential election from tweets on the Twitter application. Tweets on Twitter are written, typed and published by Indonesian netizens about the candidates who are likely to advance in the 2024 presidential election. In this study, researchers used tools, namely RapidMiner Studio to collect tweet data from Indonesian netizens about the candidates. Furthermore, the researcher uses the Naïve Bayes Classifier algorithm to determine whether a statement or sentiment has a positive or negative value which is carried out using Rapid Miner tools as well. Of the four candidates that the researchers examined, Anies got 74% positive sentiment 26% negative sentiment, then followed by Sandi, namely 57% positive sentiment 43% negative sentiment, Ganjar received 53% positive sentiment 47% negative sentiment and Prabowo received 32% positive sentiment. 68% negative sentiment. The conclusion of this research is to find out which candidates are liked or favored by the Indonesian people from the results of sentiment analysis using the Naïve Bayes algorithm and the tools used, namely Rapid Miner.
Analisis Sentimen Masyarakat Mengenai RUU Perampasan Aset Di Twitter Menggunakan Metode Naïve Bayes kivandi Nugroho; Firman Noor Hasan
SMATIKA JURNAL : STIKI Informatika Jurnal Vol 13 No 02 (2023): SMATIKA Jurnal : STIKI Informatika Jurnal
Publisher : LPPM UBHINUS MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/smatika.v13i02.899

Abstract

The development of the world is growing especially in social media, one of which is Twitter. Twitter itself is a social media that can be accessed by various groups to communicate, besides that there are various kinds of public opinions that are quite varied. Data collection from Twitter can be used to conduct sentiment analysis in order to find out public opinion. The research conducted by researchers is an analysis of public sentiment regarding RUU Perampasan Aset that has never been passed. This research starts from data collection, processing, data implementation and evaluation using rapidminer tools. In the data retrieval process, researchers used the keyword "RUU Perampasan Aset", the data that was successfully obtained was 413, which was then processed at the Preprocessing stage so that later it could be analyzed using the naïve bayes method in this process 179 data were obtained. The results obtained in the form of positive and negative analysis of RUU Perampasan Asetl, obtained as many as 131 or 73% positive comments and only 48 or 27% negative comments. For positive class precision 90%, and class recall of 55%, while for negative class precision 42%, and class recall of 85%, with accuracy obtained at 63%.
Sentiment Analysis on Shopee Xpress Delivery Time Reviews Using Support Vector Machine and Logistic Regression Sewin Fathurrohman; Irfan Ricky Afandi; Irma Wahyuningtyas; Azis Styo Nugroho; Firman Noor Hasan
IJID (International Journal on Informatics for Development) Vol. 14 No. 2 (2025): IJID December
Publisher : Faculty of Science and Technology, Universitas Islam Negeri (UIN) Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/ijid.2025.5073

Abstract

This study examines user sentiment towards Shopee Xpress delivery times using machine learning techniques. We collected 497 reviews from platforms like X and the Google Play Store, leveraging the valuable feedback despite its unstructured and informal nature. After labelling 398 reviews for model training and reserving 99 for sentiment prediction, we implemented two classification algorithms: Support Vector Machine (SVM) and Logistic Regression. These models categorised sentiments into negative, neutral, and positive classes. Despite class imbalance in the training data, SVM outperformed Logistic Regression with an accuracy of 93%, demonstrating a more balanced performance across sentiment categories compared to Logistic Regression's 90% accuracy. Both models showed consistent sentiment prediction on new data. Our findings highlight the potential of sentiment analysis as a valuable tool for Shopee Xpress to understand customer perceptions and improve delivery experiences. By providing actionable insights, this study can inform logistics improvements and enhance customer satisfaction. Future research could benefit from collaborating with Shopee to access internal data and integrating additional data sources for more comprehensive insights, ultimately driving business growth and customer loyalty. This study contributes to the growing body of research on sentiment analysis in logistics and e-commerce.
Rancang Bangun Sistem Penebar Pakan Ikan berbasis Internet of Things (IoT) Ridwan Bagus Andreyanto; Gusnul Mahesa; Muhammad Rizal; Lutfi Triyuli Evana Rizki; Firman Noor Hasan
Prosiding Seminar Nasional Teknoka Vol 10 (2025): Proceeding of TEKNOKA National Seminar - 10
Publisher : Fakultas Teknik, Universitas Muhammadiyah Prof. Dr. Hamka, Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

In everyday life, both in urban and rural areas, many people enjoy keeping fish as pets because they are easy to care for and maintain. This has led to a growing interest in fishkeeping among the general public. Fish kept in aquariums require regular feeding schedules to maintain their health, making an organized and consistent feeding system essential. The ESP32 is an integrated chip developed for modern networking applications. It provides a complete and integrated Wi-Fi networking solution that can function as an application processor or serve as a Wi-Fi network controller for other processors. One of its practical applications is in the development of an automatic fish feeder based on the Internet of Things (IoT). By utilizing the ESP32 along with supporting software such as Arduino IDE, Google Firebase, and MIT App Inventor, the automatic fish feeder can operate automatically according to predefined feeding schedules. Furthermore, the system can send notifications through a website when the fish have been fed or when the feed container is empty.
Implementasi Teknik Clustering untuk Meningkatkan Performa Aplikasi Node JS Rozak, Bahrul; Erizal, Erizal; Hasan, Firman Noor
Smart Comp :Jurnalnya Orang Pintar Komputer Vol 12, No 4 (2023): Smart Comp: Jurnalnya Orang Pintar Komputer
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/smartcomp.v12i4.4532

Abstract

Permasalahan yang sering kali muncul pada aplikasi berbasis server side, adalah request request dan response dalam jumlah yang besar. Proses request serta response akan terus berlanjut selama pengguna berinteraksi dengan aplikasi, Jika hal ini terus berlanjut maka penggunaan sumber daya (resource) yang berlebih pada CPU dapat menjadikan perfoma aplikasi tidak optimal bahkan dapat menimbulkan crash, yang berdampak pada layanan dan kualitas aplikasi. Oleh karena itu, diperlukan teknik untuk menangani permasalahan tersebut. Metode yang diimplementasikan pada penelitian ini ialah membuat aplikasi berbasis server side dengan dua spesifikasi yaitu dengan module cluster dan tanpa module cluster, selanjutnya kedua aplikasi dengan spesifikasi yang berbeda, masing-masing akan dilakukan tahap pengujian performa serta monitoring, kemudian dilakukan proses analisa hasil perbandingan untuk mendapatkan kesimpulan. Module cluster akan membantu untuk melakukan teknik clustering, teknik clustering ialah mengelompokkan proses yang sama serta sering dieksekusi. Dengan teknik ini beban kerja pada CPU akan terdistribusi, sehingga memberikan peningkatan performa aplikasi.
Co-Authors Abdillah, Allif Rizki Abdul Syakir Achmad Ramadhan Achmad Sufyan Aziz Afandi, Irfan Ricky Affandi, Irfan Ricky Afikah, Prista Afnan Sabili, Dian Ainurrafik Agus Fikri Agus Fikri Ahmad Rizal Dzikrillah Ahmad Rizal Dzikrillah Ahmad Roshid Ahmad Syahril Ahmad Syahril Al Ghozi, Dhiyauddin Alfandi Safira Alim, Endy Sjaiful Allif Rizki Abdillah Allif Rizki Abdillah Allif Rizki Abdillah Ammar Rusydi Ananda Prasta Warasati Janah Ananda, Ridha Faiz Andika Saputra Andriani, Vivi Anhari, Tirta Anwar Hidayat Ari Wibowo Arief Wibowo Arien Bianingrum Rossianiz Arvin Rafialdo Aulia, Muhammad Fathan Avorizano, Arry Azhar Haikal Anwar Azhar Haikal Anwar Azis Styo Nugroho Bagas Kembar Rezkyllah Bahrul Rozak Bahrul Rozak Dan Mugisidi Dandie Triyanto Desty Afni Dewi Mayangsari Dian Ainurrafik Afnan Sabili Dian Ainurrafik Afnan Sabili Diana Fitri Lessy Diana Fitri Lessy Dimas Febriawan Dimas Febriawan Dimas Febriawan Dion Parisda Ray Djeli Moh Yusuf Doni Gunawan Rambe E Erizal Erizal Erizal Estu Sinduningrum Estu Sinduningrum Fachri Zaini Fadli Al Gani Fadli Hardiyanto Putra Fadli, Khairul Faisal Parsakh Nursyamsi Faisal Parsakh Nursyamsyi Fajar Sidik Faldy Irwiensyah Faldy Irwiensyah Faldy Irwiensyah, Faldy Farhan Bias Purnama Putra Farhan Nufairi Farhan Nufairi Fathurrohman, Sewin Fauzan Setya Ananto Fauzi Kurniawan Fayakun Kun Febriandirza, Arafat Febriawan, Dimas Gusnul Mahesa Hafizh Dhery Al Assyam Handika, Yusuf Hanif, Isa Faqihuddin Hardyatman, Intan Diah Hazbi Santoso Hibatullah Faisal Hibatullah Faisal Hibatullah Faisal Hilmi Ammar Hilmy Zhafran Muflih I Ketut Sudaryana, I Ketut Ibnu Suhada Indra Ramadhan Indra Ramadhan Indriyanti, Prastika Intania Widyaningrum Irawati Irawati Irfan Ricky Afandi Irfan Ricky Affandi Irma Wahyuningtyas Isnan Wisnu Prastiyo Kamayani, Mia kivandi Nugroho Krisna, Mohammad Dito Dwi Kurniyati Nur Lathifah Dini Rachmawati Lingga Lingga Lingga Lita Astri Pramesti Luqman Abdur Rahman Malik Lutfi Triyuli Evana Rizki Luthfi Akbar Ramadhan M. Asep Rizkiawan Meliyawati MILASARI, LISA ASTRIA Mohammad Akhdaan Juliandra Muchammad Sholeh Muchammad Sholeh Muflih, Hilmy Zhafran Muhamad Saiful Arif Muhammad Abid Fajar Muhammad Ardhi Ryan Saputra Muhammad Ghiffar Sistani Muhammad Ikhwan Muhammad Ikhwan Muhammad Rafly Al Fattah Zain Muhammad Ridwan Muhammad Rifansyah Mukti, Avis Tantra Mutiara Zahra Arifin Nisa Qonita Rizkina Nofendri, Yos Nugroho, Dendy Aprilianto Nunik Pratiwi Oktarina Heriyani Pamungkas, Dimas Panji Islami Anakku Pavita, Rachma Pranata, Ananda Bagas Prisilia Talakua Prista Afikah Purnamaningsih, Ine Rahayu Putri, Kirana Alyssa Rafli Erlangga Rahman Malik, Luqman Abdur Rahmatullah, Ahmad Faiz Ramadhita, Nindia Fitri Ramzah, Harry Reisa Inayah Rian gustini Ridwan Bagus Andreyanto Ridwan Maulana Subekti Rika Nurhayati Riyan Ariyansah Rizki Alamsyah Rizki Kamelia Rizky Ramdhani Rosalina Rosalina Rozak, Bahrul Saputra, Ramadani Sari, Jessica Windi Sari, Laila Atikah Setiawan, Ahmat Sewin Fathurrohman Simamora, Silvia Damayanti Sinduningrum, Estu Sistani, Muhammad Ghiffar Siti Nurhaliza Sri Fitriani Sunata, Muhamad Hafidz Ardian Syahri, Alfi Tasya Rizki Salsabilla Tia Anggita Sari Transiska, Dwi Wahyu Stiyawan Wahyuningtyas, Irma Wanda Aulia Widyastuti Andriyani Windi Al Azmi Wulandari, Sania Zahra, Khofifah Humaeroh Az Zaini, Fachri Zuhri Halim Zuhri Halim, Zuhri